• Advanced Photonics
  • Vol. 4, Issue 6, 066001 (2022)
Amirhossein Saba*, Carlo Gigli1、†, Ahmed B. Ayoub, and Demetri Psaltis
Author Affiliations
  • École Polytechnique Fédérale de Lausanne, Optics Laboratory, Lausanne, Switzerland
  • show less
    DOI: 10.1117/1.AP.4.6.066001 Cite this Article Set citation alerts
    Amirhossein Saba, Carlo Gigli, Ahmed B. Ayoub, Demetri Psaltis. Physics-informed neural networks for diffraction tomography[J]. Advanced Photonics, 2022, 4(6): 066001 Copy Citation Text show less
    References

    [1] W. Choi et al. Tomographic phase microscopy. Nat. Methods, 4, 717-719(2007).

    [2] Y. Sung et al. Optical diffraction tomography for high resolution live cell imaging. Opt. Express, 17, 266-277(2009).

    [3] D. Jin et al. Tomographic phase microscopy: principles and applications in bioimaging. J. Opt. Soc. Am. B, 34, B64-B77(2017).

    [4] E. Wolf. Three-dimensional structure determination of semi-transparent objects from holographic data. Opt. Commun., 1, 153-156(1969).

    [5] J. Lim et al. Comparative study of iterative reconstruction algorithms for missing cone problems in optical diffraction tomography. Opt. Express, 23, 16933-16948(2015).

    [6] U. S. Kamilov et al. Learning approach to optical tomography. Optica, 2, 517-522(2015).

    [7] S. Chowdhury et al. High-resolution 3D refractive index microscopy of multiple-scattering samples from intensity images. Optica, 6, 1211-1219(2019).

    [8] J. Lim et al. High-fidelity optical diffraction tomography of multiple scattering samples. Light Sci. Appl., 8, 1(2019).

    [9] T.-A. Pham et al. Three-dimensional optical diffraction tomography with Lippmann–Schwinger model. IEEE Trans. Comput. Imaging, 6, 727-738(2020).

    [10] G. E. Karniadakis et al. Physics-informed machine learning. Nat. Rev. Phys., 3, 422-440(2021).

    [11] S. Cai et al. Physics-informed neural networks (PINNs) for fluid mechanics: a review. Acta Mech. Sin., 37, 1727-1738(2021).

    [12] Y. Rivenson et al. Deep learning microscopy. Optica, 4, 1437-1443(2017).

    [13] N. Borhani et al. Learning to see through multimode fibers. Optica, 5, 960-966(2018).

    [14] B. Rahmani et al. Multimode optical fiber transmission with a deep learning network. Light Sci. Appl., 7, 69(2018).

    [15] Y. Rivenson et al. Phase recovery and holographic image reconstruction using deep learning in neural networks. Light Sci. Appl., 7, 17141(2018).

    [16] J. Lim, A. B. Ayoub, D. Psaltis. Three-dimensional tomography of red blood cells using deep learning. Adv. Photonics, 2, 026001(2020).

    [17] Z. Ren, Z. Xu, E. Y. Lam. End-to-end deep learning framework for digital holographic reconstruction. Adv. Photonics, 1, 016004(2019).

    [18] D. Pirone et al. Speeding up reconstruction of 3D tomograms in holographic flow cytometry via deep learning. Lab Chip, 22, 793-804(2022).

    [19] I. E. Lagaris, A. Likas, D. I. Fotiadis. Artificial neural networks for solving ordinary and partial differential equations. IEEE Trans. Neural Networks, 9, 987-1000(1998).

    [20] L. Lu et al. DeepXDE: a deep learning library for solving differential equations. SIAM Rev., 63, 208-228(2021).

    [21] M. Raissi, P. Perdikaris, G. E. Karniadakis. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys., 378, 686-707(2019).

    [22] S. M. H. Hashemi, D. Psaltis. Deep-learning PDEs with unlabeled data and hardwiring physics laws(2019).

    [23] Z. Mao, A. D. Jagtap, G. E. Karniadakis. Physics-informed neural networks for high-speed flows. Comput. Methods Appl. Mech. Eng., 360, 112789(2020).

    [24] X. Jin et al. NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations. J. Comput. Phys., 426, 109951(2021).

    [25] Y. Chen et al. Physics-informed neural networks for inverse problems in nano-optics and metamaterials. Opt. Express, 28, 11618-11633(2020).

    [26] Y. Chen, L. Dal Negro. Physics-informed neural networks for imaging and parameter retrieval of photonic nanostructures from near-field data. APL Photonics, 7, 010802(2022).

    [27] J. Lim, D. Psaltis. Maxwellnet: physics-driven deep neural network training based on maxwell’s equations. APL Photonics, 7, 011301(2022).

    [28] O. Ronneberger, P. Fischer, T. Brox. U-Net: convolutional networks for biomedical image segmentation. Lect. Notes Comput. Sci., 9351, 234-241(2015).

    [29] K. Yee. Numerical solution of initial boundary value problems involving Maxwell’s equations in isotropic media. IEEE Trans. Antennas Propag., 14, 302-307(1966).

    [30] W. C. Chew, W. H. Weedon. A 3D perfectly matched medium from modified Maxwell’s equations with stretched coordinates. Microwave Opt. Technol. Lett., 7, 599-604(1994).

    [31] A. Ishimaru. Wave Propagation and Scattering in Random Media, 2(1978).

    [32] A. Saba et al. Polarization-sensitive optical diffraction tomography. Optica, 8, 402-408(2021).

    [33] C. Tan et al. A survey on deep transfer learning. Lect. Notes Comput. Sci., 11141, 270-279(2018).

    [34] A. Fathy et al. A fourth order difference scheme for the Maxwell equations on Yee grid. J. Hyperbol. Differ. Equ., 5, 613-642(2008).

    Amirhossein Saba, Carlo Gigli, Ahmed B. Ayoub, Demetri Psaltis. Physics-informed neural networks for diffraction tomography[J]. Advanced Photonics, 2022, 4(6): 066001
    Download Citation